Data Scientist/Engineer in Financial Markets

A privat Swiss Bank is looking for a Data Scientist/Engineer in Financial Markets.

• Build data infrastructure for Trading Analytics, including Databases and Data Lake
• Develop Machine Learning and Financial models for Trading Execution, Market Making, and Sales
• Ensure data quality, including mining, cleansing, and analyzing large datasets for use-case Development 
• Write well-documented production code and algorithms, mainly in Python and Scala
• Create software applications, user interfaces, APIs, dashboards, and automated reports for business users
• Build systems for collection and transformation of large data sets to enable model development
• Understand Machine Learning literature and techniques to mentor team members
• Assist with the execution of Big Data solutions and related technologies
• Present results from AI and Financial models in a proficient manner to stakeholders
• Collaborate with Data Architects, Developers, and Business Developers to jointly meet deadlines 

• Master or PhD in a technical field, such as Mathematics, Physics, Engineering, Computer Science, or alike
• 4+ years of work experience as a Data Scientist
• Proven knowledge of Machine Learning and Artificial Intelligence, including Advanced Statistical Analysis, Data Mining, Bayesian Statistics, Reinforcement Learning, and Deep Learning
• Advanced coding skills in programming languages such as Python, R, Matlab, Scala, C++, and/or Java
• Knowledge of Webapp Development, Docker, Kubernetes, Git, and Dash (or similar)
• Knowledge of Data Architecture, Databases (including Oracle, MSSQL, and MongoDB), Data Lakes, and Cloud Computing (e.g. MS Azure or Snowflake)
• Knowledge of PowerBI, Tableau, and VBA
• Knowledge of Mathematical Finance and Financial Markets, including Algorithmic Trading, Financial Derivatives, Portfolio Management, and Dynamic Optimization methods
• Knowledge of Spark, Databricks, Kafka, and real-time analytics is a plus
• Fluency in English

Start: ASAP
Dauer: 15.09.2024
Workload: 100%